The Israel cohort consists of two databases from the Clalit Health Services (CHS) registries from Israel (CHS1 n=50,000, CHS2 n=69,594). The CHS is the largest of four operating healthcare organisations to provide healthcare to all citizens of Israel, and covers more than 50% of Israel's population. The CHS databases undergo periodic updating processes and have been validated by the registry algorithm as well as by many scientific organisations utilising the database. The diagnoses of chronic diseases are based on real-time input from healthcare providers, pharmacies, medical care facilities, and administrative computerised operating systems. Psychiatric Diagnoses are based on the ICD-9 and ICD-10 classifications. To evaluate probability of MI following infection and post-infection outcomes(CHS1), individuals with a high load of past infections will be identified and matched in a 1:1 ratio to controls by age and sex. Probability of MI onset across a broad range of psychiatric disorders will be explored among the high load and control groups, including depression, BD, anxiety and psychotic disorders. Inclusion criteria: individuals insured by the CHS since birth and with at least 10 years of follow up; exclusion criteria include termination of insurance and lack of successive medical follow up. To evaluate the probability of psychiatric relapse among individuals with pre-existing mental disorder following infection (CHS2), a cohort of 34,797 individuals with schizophrenia matched randomly to age and sex HC with no diagnosis of schizophrenia will be exploited. Inclusion criteria for this sample is an active diagnosis of schizophrenia in CHS registries during stages of analyses, and place of residence is at the CHS hospital catchment areas (to ensure psychiatric hospitalisation is fully registered); exclusion criteria include lack of active diagnosis and place of residency outside of CHS catchment area. Across both cohorts, socioeconomic status, familial status, marital status, number of siblings, smoking, obesity, diabetes, hypertension, hyperlipidemia, chronic obstructive pulmonary disease and ischemic heart disease, as well as healthcare utilisation and other demographic variables have been collected. The following infections will be considered across both cohorts: Epstein Barr Virus, Cytomegalovirus, Toxoplasma Gondii, COVID-19, and Herpes viruses. Measures of inflammation include neutrophil/lymphocyte ratio and systemic inflammatory index.
To demonstrate an association between infections, with a particular focus on viral ones, severe MI and post-infection severe outcomes (Objective 1), we will compute hazard ratios (HRs) to assess the risk of SMI development or psychiatric relapse following infection using Cox proportional hazard regression models in all three cohorts (CHS1, CHS2). Incidence rates and crude and adjusted models controlling for demographic and clinical factors will be reported. The proportional hazard assumption will be tested as the correlation between the Schoenfeld residuals and survival time, with significance level of p\<0.05 indicating non-proportionality. Estimated projections of the cumulative probability of severe outcome among individuals with pre-existing SMI will be obtained by Kaplan-Meier analysis. Confounding, moderation and mediation patterns of environmental (socio and sociodemographic) and biological (inflammatory, polygenic risk score (PRS) for MI and immune-related conditions) mechanisms will be assessed using the PROCESS macro, a simulation-based strategy based on re-sampling (bootstrapping) techniques. Direct and indirect effects, standard errors and confidence intervals will be estimated based on the bootstrap distribution found with 10,000 bias-corrected resamples. PRS will be computed following the method described by Purcell et al. Analyses will be performed based on the directed acyclic graph (DAG) causal framework, ensuring transparent model assumptions and minimising bias. All three databases provide nation-wide representative data and have proven their efficiency in characterising MI cohorts and associations with environmental factors, as shown by high impact publications .
